We begin by pairing existing inversion methods with our modified generator. Specifically, we use a ReStyle encoder [1]}, pre-trained on the human face domain. For a given real image, we first invert it using ReStyle, and then insert the inverted latent-code \(w \in \)  \(\mathcal {W}+\)  code into our modified generators.
In fig:realedits we show a series of results obtained in such a manner, using generators adapted across multiple domains. As can be seen, our generators successfully preserve the identity tied to the latent code, even for those codes obtained from the inversion of real images. As such, our model can be used for textually-driven, zero-shot out-of-domain image-to-image translation of real images (or equivalently, cross-domain editing).
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